Adaptive AR assistance can automatically trigger content to support users based on their context. Such intelligent automation offers many benefits but also alters users’ degree of control, which is seldom explored in existing research. In this paper, we compare high- and low-agency control in AR-assisted construction assembly to understand the role of user agency. We designed cognitive and physical assembly scenarios and conducted a lab study (N=24), showing that low-agency control reduced mental workloads and perceived autonomy in several tasks. A follow-up domain expert study with trained carpenters (N=8) contextualised these results in an ecologically valid setting. Through semi-structured interviews, we examined the carpenters’ perspectives on AR support in their daily work and the trade-offs of automating interactions. Based on these findings, we summarise key design considerations to inform future adaptive AR designs in the context of timber construction.
%0 Conference Paper
%1 10.1145/3706598.3713765
%A Yang, Xiliu
%A Sasikumar, Prasanth
%A Amtsberg, Felix
%A Menges, Achim
%A Sedlmair, Michael
%A Nanayakkara, Suranga
%B Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
%C New York, NY, USA
%D 2025
%I Association for Computing Machinery
%K ap29 icd intcdc mac myOwn peer rp4
%R 10.1145/3706598.3713765
%T Who is in Control? Understanding User Agency in AR-assisted Construction Assembly
%U https://doi.org/10.1145/3706598.3713765
%X Adaptive AR assistance can automatically trigger content to support users based on their context. Such intelligent automation offers many benefits but also alters users’ degree of control, which is seldom explored in existing research. In this paper, we compare high- and low-agency control in AR-assisted construction assembly to understand the role of user agency. We designed cognitive and physical assembly scenarios and conducted a lab study (N=24), showing that low-agency control reduced mental workloads and perceived autonomy in several tasks. A follow-up domain expert study with trained carpenters (N=8) contextualised these results in an ecologically valid setting. Through semi-structured interviews, we examined the carpenters’ perspectives on AR support in their daily work and the trade-offs of automating interactions. Based on these findings, we summarise key design considerations to inform future adaptive AR designs in the context of timber construction.
%@ 9798400713941
@inproceedings{10.1145/3706598.3713765,
abstract = {Adaptive AR assistance can automatically trigger content to support users based on their context. Such intelligent automation offers many benefits but also alters users’ degree of control, which is seldom explored in existing research. In this paper, we compare high- and low-agency control in AR-assisted construction assembly to understand the role of user agency. We designed cognitive and physical assembly scenarios and conducted a lab study (N=24), showing that low-agency control reduced mental workloads and perceived autonomy in several tasks. A follow-up domain expert study with trained carpenters (N=8) contextualised these results in an ecologically valid setting. Through semi-structured interviews, we examined the carpenters’ perspectives on AR support in their daily work and the trade-offs of automating interactions. Based on these findings, we summarise key design considerations to inform future adaptive AR designs in the context of timber construction.},
added-at = {2025-05-04T19:42:32.000+0200},
address = {New York, NY, USA},
articleno = {1230},
author = {Yang, Xiliu and Sasikumar, Prasanth and Amtsberg, Felix and Menges, Achim and Sedlmair, Michael and Nanayakkara, Suranga},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2af6cd3fd227e946a7f64a6b69b6ce9f4/xyang},
booktitle = {Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems},
doi = {10.1145/3706598.3713765},
interhash = {695ee3fc50f8a25838a7359b3d6005b0},
intrahash = {af6cd3fd227e946a7f64a6b69b6ce9f4},
isbn = {9798400713941},
keywords = {ap29 icd intcdc mac myOwn peer rp4},
numpages = {15},
publisher = {Association for Computing Machinery},
series = {CHI '25},
timestamp = {2025-05-04T19:43:02.000+0200},
title = {Who is in Control? Understanding User Agency in AR-assisted Construction Assembly},
url = {https://doi.org/10.1145/3706598.3713765},
year = 2025
}
